6A.[Omics_revamped]Revamped_success_analyses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d787ec75-5fc2-44f4-b62a-6128bed29618",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "library(plyr)\n",
    "library(tidyverse)\n",
    "library(ggpubr)\n",
    "library(plotly)\n",
    "library(caret)\n",
    "library(htmlwidgets)\n",
    "source(\"utils/plot_theme.r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ca2d177-8755-4434-bb06-4afcacc9c2a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "enrichr_adj_pval_thresh = 0.05\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "515aa1cb-89f6-4a06-ba39-09c35ac9c843",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22mNew names:\n",
      "\u001b[36m•\u001b[39m `` -> `...1`\n",
      "\u001b[36m•\u001b[39m `...1` -> `...2`\n",
      "\u001b[1mRows: \u001b[22m\u001b[34m300\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m25\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \"\\t\"\n",
      "\u001b[31mchr\u001b[39m (13): Source, GeneSetID, GeneSetName, GeneList, LLM Name, LLM Analysis, ...\n",
      "\u001b[32mdbl\u001b[39m (10): ...1, ...2, Unnamed: 0, n_Genes, Score, Rank, P-value, Adjusted P-...\n",
      "\u001b[33mlgl\u001b[39m  (2): LLM_success_TF, enrichr_success_TF\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    }
   ],
   "source": [
    "geneSets_DF = read_delim(file = \"data/omics_revamped_LLM_Enrichr_simVals_failure_refs_DF.tsv\",\n",
    "                         delim = \"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d562f83-79b4-465b-869e-bd28476586a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "geneSets_DF$LLM_success_TF = as.factor(geneSets_DF$LLM_success_TF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "67843057-f564-405d-833b-40642ecdb35e",
   "metadata": {},
   "outputs": [],
   "source": [
    "JI_threshList = seq(0, 1, 0.05) #seq(0, 1, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f96286b-b76d-4df4-823e-42a242369768",
   "metadata": {},
   "outputs": [],
   "source": [
    "JI_thresh_eval_DF = data_frame(JI_thresh = JI_threshList, perc_success = NaN, accuracy = NaN, perc_meetsThresh = NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "49280265-44bc-4f35-8432-c06ac2607377",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n",
      "Warning message in confusionMatrix.default(JI_threshCol, geneSets_DF$LLM_success_TF):\n",
      "“Levels are not in the same order for reference and data. Refactoring data to match.”\n"
     ]
    }
   ],
   "source": [
    "for (JI_threshInd in c(1:length(JI_threshList))){\n",
    "    JI_thresh = JI_threshList[JI_threshInd]\n",
    "    \n",
    "    JI_threshCol = ((geneSets_DF$`Adjusted P-value` <= enrichr_adj_pval_thresh) & (geneSets_DF$enrichr_JI >= JI_thresh)) \n",
    "\n",
    "    JI_thresh_eval_DF$perc_success[JI_threshInd] = sum(JI_threshCol)/nrow(geneSets_DF)\n",
    "    \n",
    "    JI_threshOnlyCol = (geneSets_DF$enrichr_JI >= JI_thresh) \n",
    "\n",
    "    JI_thresh_eval_DF$perc_meetsThresh[JI_threshInd] = 100*sum(JI_threshOnlyCol)/nrow(geneSets_DF)\n",
    "    \n",
    "    \n",
    "    JI_threshCol = JI_threshCol %>% as.factor()\n",
    "    \n",
    "    c = confusionMatrix(JI_threshCol, geneSets_DF$LLM_success_TF)\n",
    "    JI_thresh_eval_DF$accuracy[JI_threshInd] = c$overall[\"Accuracy\"]\n",
    "    \n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d25beb87-fca8-476b-b428-8de59b60d22f",
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = ggscatter(data = JI_thresh_eval_DF,\n",
    "          x = \"JI_thresh\",\n",
    "          y = \"perc_meetsThresh\", \n",
    "          xlab = \"Required overlap with enriched GO terms\",\n",
    "        ylab = \"Percentage of omics gene sets with required overlap with GO\") +\n",
    "geom_line()+\n",
    "geom_vline(xintercept = 0.1, color = \"red\", linetype=\"dotted\")+\n",
    "theme_pubr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8766a926-9b9f-404a-a967-04acf216a71b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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T3dXCheKtlFR5WrIIAAAggggAACCPhdoEwJ0tatW+Wxxx4zBRqWLVtmbD799FM5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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a348ad42-e29c-4d0f-9f39-c0812490d96b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(filename = \"figures/OmicsRevamped_Enrichment_success_onlyThreshold.png\", plot = g2,width = 5, height = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "db255ea6-4b3d-4d1a-be03-0a5ff11c6bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(filename = \"figures/OmicsRevamped_Enrichment_success_onlyThreshold.svg\", plot = g2,width = 5, height = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0db1b3e8-78b9-4874-9189-ae2aa14a47bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4109400-ea37-4c72-b801-7d19bcf40967",
   "metadata": {},
   "outputs": [],
   "source": [
    "geneSets_DF$enrichr_success_TF_0.1 = ((geneSets_DF$`Adjusted P-value` <= enrichr_adj_pval_thresh) & (geneSets_DF$enrichr_JI >= 0.1)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef92d890-8d8d-4aca-81c3-b74f534aed86",
   "metadata": {},
   "outputs": [],
   "source": [
    "write_delim(x = geneSets_DF, file = \"data/omics_revamped_LLM_Enrichr_simVals_failure_refs_newThresh_DF.tsv\" , \n",
    "            delim = \"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c4e15d7-8db1-489a-a836-ab7df0e9099c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "r"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}